Support for hybrid search in Azure AI vector store (#2408)

Co-authored-by: Deshraj Yadav <deshrajdry@gmail.com>

docs/components/vectordbs/dbs/azure_ai_search.mdx

@@ -50,6 +50,24 @@ config = {
 }
 ```
 
+## Using hybrid search
+
+```python
+config = {
+    "vector_store": {
+        "provider": "azure_ai_search",
+        "config": {
+            "service_name": "ai-search-test",
+            "api_key": "*****",
+            "collection_name": "mem0", 
+            "embedding_model_dims": 1536,
+            "hybrid_search": True,
+            "vector_filter_mode": "postFilter"
+        }
+    }
+}
+```
+
 ## Configuration Parameters
 
 | Parameter | Description | Default Value | Options |
@@ -60,6 +78,8 @@ config = {
 | `embedding_model_dims` | Dimensions of the embedding model | `1536` | Any integer value |
 | `compression_type` | Type of vector compression to use | `none` | `none`, `scalar`, `binary` |
 | `use_float16` | Store vectors in half precision (Edm.Half) | `False` | `True`, `False` |
+| `vector_filter_mode` | Vector filter mode to use | `preFilter` | `postFilter`, `preFilter` |
+| `hybrid_search` | Use hybrid search | `False` | `True`, `False` |
 
 ## Notes on Configuration Options
 
@@ -68,6 +88,10 @@ config = {
   - `scalar`: Scalar quantization with reasonable balance of speed and accuracy
   - `binary`: Binary quantization for maximum compression with some accuracy trade-off
 
+- **vector_filter_mode**:
+  - `preFilter`: Applies filters before vector search (faster)
+  - `postFilter`: Applies filters after vector search (may provide better relevance)
+
 - **use_float16**: Using half precision (float16) reduces storage requirements but may slightly impact accuracy. Useful for very large vector collections.
 
 - **Filterable Fields**: The implementation automatically extracts `user_id`, `run_id`, and `agent_id` fields from payloads for filtering.